As this is the first set exercises in this workshop, it is relatively easy and short. Its purpose is also to get used to this exercise format.

You can find the solutions for this exercise and all other exercises in the ./solutions folder in the repo/directory that contains the course materials. You can easily copy code from these solution files by clicking on the small blue clipboard icon in the upper right corner of the solution boxes showing the code.

For working on the exercises, we suggest that you write R scripts (one per set of exercises or a combined one for the whole course) and store them in the ./MY_CODE folder contained in the course materials. This folder already contains a script stub called my_script.R which you can use for getting started.

NB: For the code from the solutions to work, you have to make sure that your script uses the root directory of the course materials as working directory. You can check the current working directory with getwd() and set it with setwd() accordingly. If you work with RStudio, you can also simply open the tidyverse-workshop-esra-2021.Rproj file. In that case, the correct to the working directory is already set.

That being said, let’s delve into the exercises:

1

Please install and/or load the tidyverse package.

We always recommend using the easypackages packages, which can be installed with the command install.packages("easypackages"). After loading the package with library(easypackages) you can load and install packages (if necessary) with one command: easypackages::packages("fancy_package_1", "fancy_package_2", ...)

solution 

easypackages::packages(
  "tidyverse"
)
## All packages loaded successfully

2

Load the data set named USAarrests that is built into R and print it.

You first need to load the data into your environment using the data() command. Then you can either just run the name of the data set as a command or use the print() function.

solution 

data("USArrests")

USArrests
##                Murder Assault UrbanPop Rape
## Alabama          13.2     236       58 21.2
## Alaska           10.0     263       48 44.5
## Arizona           8.1     294       80 31.0
## Arkansas          8.8     190       50 19.5
## California        9.0     276       91 40.6
## Colorado          7.9     204       78 38.7
## Connecticut       3.3     110       77 11.1
## Delaware          5.9     238       72 15.8
## Florida          15.4     335       80 31.9
## Georgia          17.4     211       60 25.8
## Hawaii            5.3      46       83 20.2
## Idaho             2.6     120       54 14.2
## Illinois         10.4     249       83 24.0
## Indiana           7.2     113       65 21.0
## Iowa              2.2      56       57 11.3
## Kansas            6.0     115       66 18.0
## Kentucky          9.7     109       52 16.3
## Louisiana        15.4     249       66 22.2
## Maine             2.1      83       51  7.8
## Maryland         11.3     300       67 27.8
## Massachusetts     4.4     149       85 16.3
## Michigan         12.1     255       74 35.1
## Minnesota         2.7      72       66 14.9
## Mississippi      16.1     259       44 17.1
## Missouri          9.0     178       70 28.2
## Montana           6.0     109       53 16.4
## Nebraska          4.3     102       62 16.5
## Nevada           12.2     252       81 46.0
## New Hampshire     2.1      57       56  9.5
## New Jersey        7.4     159       89 18.8
## New Mexico       11.4     285       70 32.1
## New York         11.1     254       86 26.1
## North Carolina   13.0     337       45 16.1
## North Dakota      0.8      45       44  7.3
## Ohio              7.3     120       75 21.4
## Oklahoma          6.6     151       68 20.0
## Oregon            4.9     159       67 29.3
## Pennsylvania      6.3     106       72 14.9
## Rhode Island      3.4     174       87  8.3
## South Carolina   14.4     279       48 22.5
## South Dakota      3.8      86       45 12.8
## Tennessee        13.2     188       59 26.9
## Texas            12.7     201       80 25.5
## Utah              3.2     120       80 22.9
## Vermont           2.2      48       32 11.2
## Virginia          8.5     156       63 20.7
## Washington        4.0     145       73 26.2
## West Virginia     5.7      81       39  9.3
## Wisconsin         2.6      53       66 10.8
## Wyoming           6.8     161       60 15.6

3

Now, convert the data to a tibble and print it. Compare it to the previous output.

You need the as_tibble() function from the tibble package here.

solution 

USArrests_tibble <-
  tibble::as_tibble(USArrests)

USArrests_tibble
## # A tibble: 50 x 4
##    Murder Assault UrbanPop  Rape
##     <dbl>   <int>    <int> <dbl>
##  1   13.2     236       58  21.2
##  2   10       263       48  44.5
##  3    8.1     294       80  31  
##  4    8.8     190       50  19.5
##  5    9       276       91  40.6
##  6    7.9     204       78  38.7
##  7    3.3     110       77  11.1
##  8    5.9     238       72  15.8
##  9   15.4     335       80  31.9
## 10   17.4     211       60  25.8
## # ... with 40 more rows

4

Have a look at the tibble using the function glimpse() from the dplyr package. Compare it to the previous outputs.

The argument that you need to provide to glimpse() is the name of the dataframe/tibble.

solution 

dplyr::glimpse(USArrests_tibble)
## Rows: 50
## Columns: 4
## $ Murder   <dbl> 13.2, 10.0, 8.1, 8.8, 9.0, 7.9, 3.3, 5.9, 15.4, 17.4, 5.3, 2.6, 10.4, 7.2, 2.2, 6.0, 9.7, 15.4, 2.1, 11.3, 4.4, 12.~
## $ Assault  <int> 236, 263, 294, 190, 276, 204, 110, 238, 335, 211, 46, 120, 249, 113, 56, 115, 109, 249, 83, 300, 149, 255, 72, 259,~
## $ UrbanPop <int> 58, 48, 80, 50, 91, 78, 77, 72, 80, 60, 83, 54, 83, 65, 57, 66, 52, 66, 51, 67, 85, 74, 66, 44, 70, 53, 62, 81, 56,~
## $ Rape     <dbl> 21.2, 44.5, 31.0, 19.5, 40.6, 38.7, 11.1, 15.8, 31.9, 25.8, 20.2, 14.2, 24.0, 21.0, 11.3, 18.0, 16.3, 22.2, 7.8, 27~
# dplyr::glimpse() provides another method of displaying the data. In such small 
# data sets, in doesn't make a huge difference. However, as the outputs of tibbles are 
# reduced for large data sets, it provides a convenient method of getting a quick 
# glimpse (pun intended) of the data.

5

Finally, reload the data, convert it to a tibble, and use dplyr::glimpse() in one combined %>%-workflow.

Remember that the data object precedes the function call: x %>% f(.)

solution 

data("USArrests")

USArrests %>% 
  tibble::as_tibble() %>% 
  dplyr::glimpse()
## Rows: 50
## Columns: 4
## $ Murder   <dbl> 13.2, 10.0, 8.1, 8.8, 9.0, 7.9, 3.3, 5.9, 15.4, 17.4, 5.3, 2.6, 10.4, 7.2, 2.2, 6.0, 9.7, 15.4, 2.1, 11.3, 4.4, 12.~
## $ Assault  <int> 236, 263, 294, 190, 276, 204, 110, 238, 335, 211, 46, 120, 249, 113, 56, 115, 109, 249, 83, 300, 149, 255, 72, 259,~
## $ UrbanPop <int> 58, 48, 80, 50, 91, 78, 77, 72, 80, 60, 83, 54, 83, 65, 57, 66, 52, 66, 51, 67, 85, 74, 66, 44, 70, 53, 62, 81, 56,~
## $ Rape     <dbl> 21.2, 44.5, 31.0, 19.5, 40.6, 38.7, 11.1, 15.8, 31.9, 25.8, 20.2, 14.2, 24.0, 21.0, 11.3, 18.0, 16.3, 22.2, 7.8, 27~